Deep Learning Applications and Challenges
Currently, deep learning is a current and a stimulating field of machine learning. Deep learning is the most effective, supervised, time and cost-efficient machine learning approach. Deep learning is not a constrained learning approach, but it bears various measures and features which can be applied to a massive speculum of intricate problems. The technique learns the explanatory and discrepancy features in a very stratified way. Deep learning methods have made a significant break- through with appreciable performance in a widespread variety of applications with useful security tools. It is considered to be the best choice for determining complex architecture in high-dimensional data by engaging back propagation algorithm. As deep learning has made significant developments and incredible performance in numerous applications, the widely used domains of deep learning are business, science and government which further includes adaptive testing, biological image classification, computer vision, cancer detection, natural language processing, object detection, face recognition, handwriting recognition, speech recognition, stock market analysis and many more. This paper emphases on the concepts of deep learning, its basic and advanced architectures, techniques, motivational aspects, characteristics and the limitations. The paper also presents the major differences between the deep learning, classical machine learning and conventional learning approaches and the major challenges ahead. The main intention of this paper is to explore and present chronologically, a comprehensive survey of the major applications of deep learning covering variety of areas, study of the techniques and architectures used and further the contribution of that respective application in the real world. Finally, the paper ends with the conclusion and future aspects.